The text discusses the concept of Retrieval Augmented Generation (RAG) applications, which are a type of Generative Artificial Intelligence (Gen AI) system that uses contextual information to answer user questions. RAG systems aim to provide more accurate and relevant responses by combining human-provided context with large language models' capabilities. The article highlights the challenges of building such systems, including data retrieval, indexing, and search techniques, as well as the importance of choosing the right vector database and embedding model. It also touches on the need for user query rewriting, hybrid search, reranking, and reducing duplicates to improve system performance. The author emphasizes that RAG development is similar to software development and requires careful consideration of various factors to create a production-worthy system.